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Biologically Inspired Architectures for Learning and Control
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Part II - Case Studies

This article reviews the state of the art in biologically inspired learning architectures for autonomous systems, and discusses the major issues in three core learning techniques: reinforcement learning, neural-network-based learning, and learning using evolutionary algorithms. To illustrate implementations of these robot learning approaches, the following cases are studied: Pattern classification with a recurrent neural network (RNN); Autonomous land vehicle in neural networks (ALVINN); Obstacle avoidance using reinforcement learning (RL) Some of the limitations of cognitive learning processes in real world situations and directions for future development are also discussed.